Collaborative Research: Integrated HPC Systems Usage and Performance of Resources Monitoring and Modeling (SUPReMM)

协作研究：集成 HPC 系统资源使用和性能监控和建模 (SUPReMM)

• 批准号: 1203560
• 负责人: Abani Patra
• 金额: $ 45.86万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1203560&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrated; HPC; Systems

Todays high-performance computing systems are a complex combination of software, processors, memory, networks, and storage systems characterized by frequent disruptive technological advances. In this environment, system managers, users and sponsors find it difficult if not impossible to know if optimal performance of the infrastructure is being realized, or even if all subcomponents are functioning properly. Users of such systems are often engaged in science at the extreme where system uncertainties can significantly delay or even confound the scientific investigations. Critically, for systems based on open source software systems which includes a large fraction of XSEDE resources, the data and information necessary to use and manage these complex systems is not available. HPC centers and their users, are to some extent flying blind, without a clear understanding of system behavior. Anomalous behavior has to be diagnosed and remedied with incomplete and sparse data. It is difficult for users to assess the effectiveness with which they are using the available resources to generate knowledge in their sciences. NSF lacks a comprehensive knowledge base to evaluate the effectiveness of its investments in HPC systems.This award will address this problem through the creation of a comprehensive set of tools for developing the needed knowledge bases. This will be accomplished by building on and combining work on HPC systems monitoring and reporting currently underway at the University at Buffalo under the Technology Audit Service (TAS) of the XSEDE project and University of Texas/ Texas Advance Computing Center (TACC) as part of the Ranger Technology Insertion effort with many elements of existing monitoring and analysis tools. The PIs will provide the knowledge bases required to understand the current operations of XSEDE, to enhance and increase the productivity of all of the stakeholders of XSEDE (service providers, users and sponsors), and ultimately to provide open source tools to greatly increase the operational efficiency and productivity of HPC systems in general.

当今的高性能计算系统是软件、处理器、内存、网络和存储系统的复杂组合，其特点是频繁的颠覆性技术进步。在这种环境中，系统管理员、用户和赞助商发现很难甚至不可能知道基础设施是否实现了最佳性能，或者即使所有子组件都正常运行。此类系统的用户通常从事极端的科学研究，系统的不确定性可能会严重延迟甚至混淆科学研究。至关重要的是，对于基于包含大部分 XSEDE 资源的开源软件系统的系统，使用和管理这些复杂系统所需的数据和信息不可用。 HPC 中心及其用户在某种程度上是盲目的，没有清楚地了解系统行为。必须使用不完整和稀疏的数据来诊断和纠正异常行为。用户很难评估他们使用可用资源生成科学知识的有效性。 NSF 缺乏全面的知识库来评估其 HPC 系统投资的有效性。该奖项将通过创建一套全面的工具来开发所需的知识库来解决这个问题。这将通过建立和结合目前在 XSEDE 项目的技术审计服务 (TAS) 下在布法罗大学进行的 HPC 系统监控和报告工作以及德克萨斯大学/德克萨斯高级计算中心 (TACC) 的工作来实现，作为Ranger 技术插入工作与现有监控和分析工具的许多要素相结合。 PI 将提供了解 XSEDE 当前运营所需的知识库，以增强和提高 XSEDE 所有利益相关者（服务提供商、用户和赞助商）的生产力，并最终提供开源工具来大大提高运营效率HPC 系统的总体效率和生产力。